Extracting card identification data

ABSTRACT

Extracting card information comprises a server at an optical character recognition (“OCR”) system that interprets data from a card. The OCR system performs an optical character recognition algorithm an image of a card and performs a data recognition algorithm on a machine-readable code on the image of the card. The OCR system compares a series of extracted alphanumeric characters obtained via the optical character recognition process to data extracted from the machine-readable code via the data recognition process and matches the alphanumeric series of characters to a particular series of characters extracted from the machine-readable code. The OCR system determines if the alphanumeric series and the matching series of characters extracted from the machine-readable code comprise any discrepancies and corrects the alphanumeric series of characters based on the particular series of characters extracted from the machine-readable code upon a determination that a discrepancy exists.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 14/550,871, filed Nov. 21, 2014, and entitled“Extracting Card Identification Data,” which claims priority under toU.S. Patent Application No. 62/024,830 filed Jul. 15, 2014 and entitled“Extracting Card Identification Data.” The entire disclosure of each ofthe above-identified priority applications is hereby fully incorporatedherein by reference.

TECHNICAL FIELD

The technology disclosed herein pertains to extracting card information,and more particularly to using card data, barcode data, and adaptivemetadata models to improve the extraction process.

BACKGROUND

When consumers make online purchases or purchases using mobile devices,they are often forced to enter card information into the mobile devicefor payment. Due to the small screen size and keyboard interface on amobile device, such entry is generally cumbersome and prone to errors.Users may use many different cards for purchases, such as gift cards,debit cards, credit cards, stored value cards, and other cards.Information entry difficulties are multiplied for a merchant attemptingto process mobile payments on mobile devices for multiple transactions.

Current applications for obtaining payment information from a paymentcard require a precise positioning of the card in the scan. Typically, abox is presented on the user interface of the user computing device. Theuser is required to precisely line the card up within the box to allowthe user computing device to recognize the card.

Even with proper techniques, current optical character recognitionprocesses, systems, and algorithms, are occasionally prone to extractingincorrect data from the card images. Current applications do not utilizeother available data, such as barcode data to improve the extractionprocess.

SUMMARY

Techniques herein provide a computer-implemented method to extract cardinformation from a digital image of a card. In an example embodiment, anoptical character recognition (“OCR”) system performs an opticalcharacter recognition algorithm on an image of a card and performs adata recognition algorithm on a machine-readable code on the image ofthe card. The OCR system compares a series of extracted alphanumericcharacters obtained via the optical character recognition process todata extracted from the machine-readable code via the data recognitionprocess and matches the alphanumeric series of characters to aparticular series of characters extracted from the machine-readablecode. The OCR system determines if the alphanumeric series and thematching series of characters extracted from the machine-readable codecomprise any discrepancies and corrects the alphanumeric series ofcharacters based on the particular series of characters extracted fromthe machine-readable code upon a determination that a discrepancyexists. The OCR system may also determine a card type and use a cardformat associated with the card type to improve the data extraction.

In certain other example aspects described herein, a system and acomputer program product to extract account information from a card areprovided.

These and other aspects, objects, features, and advantages of theexample embodiments will become apparent to those having ordinary skillin the art upon consideration of the following detailed description ofillustrated example embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram depicting a system for extracting accountinformation from a card, in accordance with certain example embodimentsof the technology disclosed herein.

FIG. 2 is a block flow diagram depicting methods for extracting accountinformation using a comparison with barcode data, in accordance withcertain example embodiments.

FIG. 3 is a block flow diagram depicting methods for using classifiermodels to provide card metadata, in accordance with certain exampleembodiments.

FIG. 4 is a block flow diagram depicting methods for updating aclassifier model, in accordance with certain example embodiments.

FIG. 5 is a block flow diagram depicting methods for extracting carddata using a determined card type, in accordance with certain exampleembodiments.

FIG. 6 is an illustration of a user computing device displaying an imageof a gift card, in accordance with certain example embodiments.

FIG. 7 is a block flow diagram depicting methods for identifyingmatching data groups in a comparison of an extracted identification codeto extracted barcode data, in accordance with certain exampleembodiments.

FIG. 8 is a block flow diagram depicting methods for determining whethertext or barcode data is a more accurate card identification location, inaccordance with certain example embodiments.

FIG. 9 is a block diagram depicting a computing machine and a module, inaccordance with certain example embodiments.

DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS

Overview

Embodiments herein provide computer-implemented techniques for allowinga user computing device to extract card information using opticalcharacter recognition (“OCR”). Throughout the specification, the generalterm “card” will be used to represent any type of physical cardinstrument, such as a magnetic stripe card. In example embodiments, thedifferent types of card represented by “card” can include credit cards,debit cards, stored value cards, loyalty cards, identification cards, orany other suitable card representing an account or other record of auser or other information thereon. Example embodiments described hereinmay be applied to the images of other items, such as receipts, boardingpasses, tickets, and other suitable items. The card may also be an imageor facsimile of the card. For example, the card may be a representationof a card on a display screen or a printed image of a card.

The user may employ the card when making a transaction, such as apurchase, ticketed entry, loyalty check-in, or other suitabletransaction. The user may obtain the card information for the purpose ofimporting the account represented by the card into a digital walletapplication module of a computing device or for other digital accountpurposes. The card is typically a plastic card containing the redemptioncode, account information, and other data. In certain card embodiments,the customer name, expiration date, and card numbers are physicallyembossed or otherwise written on the card. The embossed information isvisible from both the front and back of the card, although the embossedinformation is typically reversed on the back of the card.

A user may desire to enter the information from the card into a mobileuser computing device or other computing device, for example, to conductan online purchase, to conduct a purchase at a merchant location, to addthe information to a wallet application on a user computing device, orfor any other suitable reason. In an example, the user desires to use amobile computing device to conduct a purchase transaction using adigital wallet application module executing on the mobile computingdevice. The digital wallet application module may require an input ofthe details of a particular gift card or other user payment account toconduct a transaction with the particular user payment account or to setup the account. Due to the small screen size and keyboard interface on amobile device, such entry can be cumbersome and error prone for manualinput. Additionally, a merchant system may need to capture cardinformation to conduct a transaction or for other reasons.

In addition to account identifiers, the front of the card typicallycontains logos of the issuer of the card, pictures chosen by the user orthe issuer, other text describing the type or status of the useraccount, redemption rules, a security code, and other marketing andsecurity elements, such as holograms or badges. The name, cardexpiration date, and the account identifier, such as a credit cardnumber, may be embossed on the front of the card such that theinformation protrudes from the front of the card.

The user employs a mobile phone, digital camera, or other user computingdevice to capture an image of the card associated with the account thatthe user desires to input into the user computing device. An OCRapplication receives the image of the card from the camera. The OCRapplication communicates the image to the OCR system. The OCR systemisolates the image of the card. The OCR system can use any image datamanipulation or image extraction to isolate the card image. The OCRsystem may crop out a portion of the image to display only the desiredinformation from the card.

The OCR system extracts relevant data from the card. The extraction maybe performed with an OCR algorithm, image recognition, or any suitablemanipulation or analysis of the image. The OCR system may extract thehuman-readable identifier, the user name, a merchant name, legal termsfor redemption or usage, images, logos, or any other suitableinformation or data from the card. In particular, the OCR systemidentifies a likely human-readable identifier and a machine-readableidentifier, such as a barcode. The human-readable identifier may be aseries of alphanumeric characters that form a redemption code, anaccount identifier, or any suitable identifier. The barcode may includea redemption code, an account identifier, and/or any suitableidentifier, in addition to other data.

The OCR system compares the extracted alphanumeric characters to theextracted barcode data to verify or correct the characters. Theidentifier of the card, such as a redemption code or an accountidentifier, is typically represented on the card as an alphanumericcharacter series and in the barcode. Data extracted from a barcode istypically more accurate than data extracted from alphanumericcharacters.

In an example, a 16 digit redemption code for a gift card may beextracted and compared to a 30 digit code extracted from a barcode. TheOCR system may employ a matching algorithm to identify a 16 digit seriesembedded in the 30 digit code from the barcode that matches theredemption code.

If the 16 digit series in the barcode matches the 16 digit alphanumericcharacter series, then the redemption code of the example is verified asaccurate. In certain examples, the digits are not an exact match. In anexample, 14 of the digits match, but 2 of the digits are different. Ifone or more of the digits do not match, the barcode digits are assumedto be correct and the alphanumeric characters are corrected accordingly.

In another embodiment, the OCR system uses text extracted from the cardimage to train and retrain card metadata models. The OCR system may sortcard data that may be used to identify a card type or a particularmerchant system associated with the card. Card data that may be used toidentify a card type might include a merchant name, an issuer name, aphone number, a website address, an address, a logo, or any othersuitable data. In certain embodiments, other data extracted from thecard image may be used, such as pictures, logos, or other suitable data.

When the OCR system extracts data from a subsequent card image, theextracted data is compared to the metadata model database. The data fromthe card image is classified based on matches between the extracted dataand the metadata models. For example, if the extracted data comprises aspecific merchant system name, then the model with that name may beidentified as an appropriate model for the card. Metadata for the cardtype associated with the merchant system may be provided to the OCRsystem. For example, the OCR system may extract, from the model, amerchant system address or a merchant system website address.

The metadata may be used to autofill a form that is being used to addthe card to the digital wallet application module of the user.Autofilling a form includes any process or method to populate data entryrequirements on a form or other online document with expected entriesbased on stored or supplied information without requiring an input froma user.

In certain examples, the user corrects the autofilled data. In anexample, the data that was inserted into a data field of the form iseither incorrect or not the preferred entry for the user. In theexample, the user manually alters the data in the incorrect field. Theuser saves the corrected data and registers the card or performs anyother suitable action with the card data.

The OCR system logs the correction by the user. Periodically orcontinually, the OCR system analyzes the logged corrections. If thenumber of corrections to a data field surpasses a threshold, then theOCR system retrains the metadata model. An example threshold may betwenty users.

In an example, the OCR system determines that twenty users have changedthe phone number for a merchant system associated with a card from thestored number to an alternate number. The OCR system determines thateither the previous number was incorrect or that the merchant system haschanged the phone number. The OCR system updates the metadata modelassociated with the merchant system to reflect the changed model. TheOCR system may provide the updated phone number to subsequent users todetermine if the number is accepted by the users. Alternatively, anoperator of the OCR system verifies the data independently. If verified,then the OCR system updates the model to reflect the new phone numberand provides the new phone number to subsequent users.

In another example, the data extracted by the OCR system from a cardimage is used to identify the card type and the identity of the merchantsystem. The text may be identified in the database as the name for aparticular bank, merchant system, card issuer, or any suitableinstitution. For example, the OCR system may identify text on the cardimage that matches a name of a merchant system in the database. In anexample, the OCR system may identify the card image as likely being adebit card associated with the particular merchant system.

When the merchant system of the example is identified, the OCR systemdetermines the format of the card associated with the merchant system.The known format of a card allows the data extraction to be performedmore accurately. For example, if the OCR system determines that the cardhas a format wherein a redemption code is a 16 digit code at the end ofthe string of characters represented on the barcode, then the OCR systemwill be able to easily and accurately extract the redemption code. Inanother example, the OCR system may access a Checksum rule for theaccount number on a card. If the OCR system extracts the account number,the OCR system may then apply the appropriate Checksum algorithm toverify the account number.

In other embodiments, the OCR system may be customized to look forcharacters in particular locations on the card image based on the cardtype. The OCR system may be customized to look for a certain number ofcharacters. The OCR system may be customized to look for certaincombinations of characters. The OCR system may be customized to knowthat the cards from the particular credit card company typically havecertain data on the reverse side of the card. The OCR system may becustomized to know which characters are typically embossed. The OCRsystem may be customized to look for any configured arrangements, datalocations, limitations, card types, character configurations, or othersuitable card data.

Example System Architecture

Turning now to the drawings, in which like numerals represent like (butnot necessarily identical) elements throughout the figures, exampleembodiments are described in detail.

FIG. 1 is a block diagram depicting a system for extracting financialaccount information from a card, in accordance with certain exampleembodiments. As depicted in FIG. 1, the system 100 includes networkcomputing systems 110, 120, 140, and 170 that are configured tocommunicate with one another via one or more networks 105. In someembodiments, a user associated with a device must install an applicationand/or make a feature selection to obtain the benefits of the techniquesdescribed herein.

Each network 105 includes a wired or wireless telecommunication means bywhich network devices (including devices 110, 124, 140, and 170) canexchange data. For example, each network 105 can include a local areanetwork (“LAN”), a wide area network (“WAN”), an intranet, an Internet,a mobile telephone network, or any combination thereof. Throughout thediscussion of example embodiments, it should be understood that theterms “data” and “information” are used interchangeably herein to referto text, images, audio, video, or any other form of information that canexist in a computer-based environment.

Each network computing system 110, 120, 140, and 170 includes a devicehaving a communication module capable of transmitting and receiving dataover the network 105. For example, each network device 110, 124, 140,and 170 can include a server, desktop computer, laptop computer, tabletcomputer, a television with one or more processors embedded thereinand/or coupled thereto, smart phone, handheld computer, personal digitalassistant (“PDA”), or any other wired or wireless, processor-drivendevice. In the example embodiment depicted in FIG. 1, the networkdevices 110, 124, 140, and 170 are operated by end-users or consumers,OCR system operators, payment processing system operators, and cardissuer operators, respectively.

The user 101 can use the communication application 112, which may be,for example, a web browser application or a stand-alone application, toview, download, upload, or otherwise access documents or web pages via adistributed network 105. The network 105 includes a wired or wirelesstelecommunication system or device by which network devices (includingdevices 110, 124, 140, and 170) can exchange data. For example, thenetwork 105 can include a local area network (“LAN”), a wide areanetwork (“WAN”), an intranet, an Internet, storage area network (SAN),personal area network (PAN), a metropolitan area network (MAN), awireless local area network (WLAN), a virtual private network (VPN), acellular or other mobile communication network, Bluetooth, NFC, or anycombination thereof or any other appropriate architecture or system thatfacilitates the communication of signals, data, and/or messages.

The user device 110 may employ a communication module 112 to communicatewith the OCR system application module 124 of the OCR system 120 orother servers. The communication module 112 may allow devices tocommunicate via technologies other than the network 105. Examples mightinclude a cellular network, radio network, or other communicationnetwork.

The user computing device 110 may include a digital wallet applicationmodule 111. The digital wallet application module 111 may encompass anyapplication, hardware, software, or process the user device 110 mayemploy to assist the user 101 in completing a purchase. The digitalwallet application module 111 can interact with the communicationapplication 112 or can be embodied as a companion application of thecommunication application 112. As a companion application, the digitalwallet application module 111 executes within the communicationapplication 112. That is, the digital wallet application module 111 maybe an application program embedded in the communication application 112.

The user device 110 may include an optical character recognition (“OCR”)application 115. The OCR application 115 may interact with thecommunication application 112 or be embodied as a companion applicationof the communication application 112 and execute within thecommunication application 112. In an exemplary embodiment, the OCRapplication 115 may additionally or alternatively be embodied as acompanion application of the digital wallet application module 111 andexecute within the digital wallet application module 111. The OCRapplication 115 may employ a software interface that may open in thedigital wallet application 111 or may open in the communicationapplication 112. The interface can allow the user 101 to configure theOCR application 115.

The OCR application 115 may be used to analyze a card and extractinformation or other data from the card. The OCR system 120 or othersystem that develops the OCR algorithms or other methods may include aset of computer-readable program instructions, for example, usingJavaScript, that enable the OCR system 120 to interact with the OCRapplication 115.

Any of the functions described in the specification as being performedby the OCR application 115 can be performed by the payment processingsystem 140, the OCR system application module 124, the user computingdevice 110, the digital wallet application module 111, a merchant system(not pictured) or any other suitable hardware or software system orapplication. In an example, the OCR application 115 on the usercomputing device 110 may obtain an image of a card 102 and transmit theimage to the OCR system application module 124 to extract theinformation on the card 102.

The user device 110 includes a data storage unit 113 accessible by theOCR application 115, the web browser application 112, or any suitablecomputing device or application. The exemplary data storage unit 113 caninclude one or more tangible computer-readable media. The data storageunit 113 can be stored on the user device 110 or can be logicallycoupled to the user device 110. For example, the data storage unit 113can include on-board flash memory and/or one or more removable memorycards or removable flash memory.

The user device 110 may include a camera 114. The camera may be anymodule or function of the user computing device 110 that obtains adigital image. The camera 114 may be onboard the user computing device110 or in any manner logically connected to the user computing device110. The camera 114 may be capable of obtaining individual images or avideo scan. Any other suitable image capturing device may be representedby the camera 114.

The user device 110 may include user applications 116. The userapplications 116 may be contact applications, email applications,digital wallet applications, or any applications that may employ thename of the user and/or names of acquaintances of the user. The user mayprovide permission to the OCR application 115 to access the names andother data from the user applications 116. The OCR application 115 mayuse the data from the user applications 116 to verify or improve the OCRprocess.

The payment processing system 140 includes a data storage unit 147accessible by the web server 144. The example data storage unit 147 caninclude one or more tangible computer-readable storage devices. Thepayment processing system 140 is operable to conduct payments between auser 101 and a merchant system (not pictured). The payment processingsystem 140 is further operable to manage a payment account of a user101, maintain a database to store transactions of the merchant systemand the user 101, verify transactions, and other suitable functions.

The user 101 may use a web server 144 on the payment processing system140 to view, register, download, upload, or otherwise access the paymentprocessing system 140 via a website (not illustrated) and acommunication network 105. The user 101 associates one or moreregistered financial card accounts, including bank account debit cards,credit cards, gift cards, loyalty cards, coupons, offers, prepaidoffers, store rewards cards, or other type of financial account that canbe used to make a purchase or redeem value-added services with a paymentaccount of the user 101.

A card issuer, such as a bank or other institution, may be the issuer ofthe financial account being registered. For example, the card issuer maybe a gift card issuer, credit card issuer, a debit card issuer, a storedvalue issuer, a financial institution providing an account, or any otherprovider of a financial account. The payment processing system 140 alsomay function as the issuer for the associated financial account. Theuser's registration information is saved in the payment processingsystem's 140 data storage unit 147 and is accessible the by web server144. The card issuer employs a card issuer system 170 to issue thecards, manage the user account, and perform any other suitablefunctions. The card issuer system 170 may alternatively issue cards usedfor identification, access, verification, ticketing, or cards for anysuitable purpose.

The OCR system 120 utilizes an OCR system application module 124operating a system that produces, manages, stores, or maintains OCRalgorithms, methods, processes, or services. The OCR system applicationmodule 124 may represent the computer-implemented system that the OCRsystem 120 employs to provide OCR services to user computing devices110, merchant computing systems, or any suitable entity. The OCR systemapplication module 124 can communicate with one or more paymentprocessing systems 140, a user computing device 110, or other computingdevices via any available technologies. Such technologies may include,for example, an Internet connection via the network 105, email, text,instant messaging, or other suitable communication technologies. The OCRsystem 120 may include a data storage unit 127 accessible by the OCRsystem application module 124 of the OCR system 120. The data storageunit 127 can include one or more tangible computer-readable storagedevices.

Any of the functions described in the specification as being performedby the OCR system 120 can be performed by the OCR application 115, theuser computing device 110, or any other suitable hardware or softwaresystem or application.

Throughout the specification, the general term “card” will be used torepresent any type of physical card instrument, such as the paymentaccount card 102. In example embodiments, the different types of card102 represented by “card” 102 can include credit cards, debit cards,stored value cards (such as “gift” cards), loyalty cards, identificationcards, or any other suitable card representing an account of a user 101or other information thereon.

The user 101 may employ the card 102 when making a transaction, such asa purchase, ticketed entry, loyalty check-in, or other suitabletransaction. The user 101 may obtain the card information for thepurpose of importing the account represented by the card 102 into adigital wallet application module 111 of a computing device 110 or forother digital account purposes. The card 102 is typically a plastic cardcontaining the account information and other data on the card 102. Inmany card 102 embodiments, the customer name, expiration date, and cardnumbers are physically embossed on the card 102. The embossedinformation is visible from both the front and back of the card 102,although the embossed information is typically reversed on the back ofthe card 102.

It will be appreciated that the network connections shown are exemplaryand other means of establishing a communications link between thecomputers and devices can be used. Moreover, those having ordinary skillin the art having the benefit of the present disclosure will appreciatethat the user device 110, OCR system 120, payment processing system 140,and card issuer system 170 illustrated in FIG. 1 can have any of severalother suitable computer system configurations. For example, a userdevice 110 embodied as a mobile phone or handheld computer may notinclude all the components described above.

Example Processes

The example methods illustrated in FIGS. 2-5 and FIG. 7-8 are describedhereinafter with respect to the components of the example operatingenvironment 100. The example methods of FIGS. 2-5 and FIG. 7-8 may alsobe performed with other systems and in other environments.

FIG. 2 is a block flow diagram depicting a method 200 for extractingaccount information using a comparison with barcode data, in accordancewith certain exemplary embodiments.

With reference to FIGS. 1 and 2, in block 205, the optical characterrecognition (“OCR”) application 115 on the user device 110 obtains adigital scan or image of a card 102. The user 101 employs a mobilephone, digital camera, or other user computing device 110 to capture animage of the card 102 associated with the account that the user 101desires to input into the user computing device 110.

An OCR application 115 on a user computing device 110 obtains the imageof the card 102. The image may be obtained from the camera module of auser computing device 110, such as the camera 114 on a mobile phone. Theimage may be obtained from a scanner coupled to the user computingdevice 110 or any other suitable digital imaging device. The image maybe obtained from video captured by the user computing device 110. Theimage may be accessed by the OCR application 115 on the user computingdevice 110 from a storage location 113 on the user computing device 110,from a remote storage location, or from any suitable location. Allsources capable of providing the image will be referred to herein as a“camera” 114. In an example embodiment, an image of the front of thecard 120, an image of the reverse of the card 120, or one or more imagesof the front and/or reverse of the card 120 are obtained.

An OCR system application module 124 receives the image of the card fromthe camera 114. The functions of the OCR application 115 and the OCRsystem 120 may be performed by any suitable module, hardware, software,or application operating on the user computing device 110, the OCRsystem application module 124 or another computing device. Some, or all,of the functions of the OCR application 115 may be performed by a remoteserver or other computing device, such as the OCR system applicationmodule 124. For example, a digital wallet application module 111 on theuser computing device 110 may obtain the image of the card and transmitthe image to the OCR system application module 124 for processing. Inanother example, some of the OCR functions may be conducted by the usercomputing device 110 and some by the OCR system application module 124or another remote server. Examples provided herein may indicate thatmany of the functions are performed by an OCR system application module124, but some or all of the functions may be performed by any suitablecomputing device.

In an example, the image is presented on the user interface of the usercomputing device 110 as a live video image of the card 102 or a singleimage of the card 102. The OCR application 115 can isolate and store oneor more images from the video feed of the camera 114. For example, theuser 101 may hover the camera 114 function of a user computing device110 over a card and observe the representation of the card on the userinterface of the user computing device 110. An illustration of the card102 displayed on the user computing device is presented in FIG. 6.

FIG. 6 is an illustration of a user computing device 110 displaying animage of a gift card, in accordance with certain example embodiments.The user computing device 110 is shown as a mobile smartphone. The usercomputing device 110 is shown with a display screen 905 as a userinterface. The card 102 is shown displayed on the user computing device110. The example card 102 is a gift card displaying information such asa merchant name, a merchant website address, a barcode, a redemptioncode, and other information.

Returning to FIG. 2, in block 210, the OCR application 115 isolates theimage of the card and transmits the image to the OCR system applicationmodule 124. The image may be transmitted via an Internet connection overthe Internet, via email, via text, or any other suitable technology. Anyimage data manipulation or image extraction may be used to isolate thecard image. For example, the OCR application 115, the camera 114 module,or the user computing device 110, or other computing device performsblur detection on the image. The image may be recognized as blurry,overly bright, overly dark, or otherwise obscured in a manner thatprevents a high resolution image from being obtained. The OCRapplication 115, or other computing device, may adjust the imagecapturing method to reduce the blur in the image. For example, the OCRapplication 115 may direct the camera 114 to adjust the focus on thefinancial card. In another example, the OCR application 115 may directthe user 101 to move the camera 114 closer to, or farther away from, thecard 102. In another example, the OCR application 115 may perform adigital image manipulation to remove the blur. Any other suitable methodof correcting a blurred, or otherwise obscured, image may be utilized.

The OCR system application module 124 may crop the image to display onlythe desired information from the card 102. For example, if the card 102in the image is a credit card, the OCR system application module 124accesses information associated with the expected location of theaccount number of a credit card. The expected location may be obtainedfrom a database of card layouts stored on the system computing device124 or in another suitable location. Credit cards, driver's licenses,loyalty cards, and other cards typically meet an industry standard forthe data locations and the layout of the card. The industry standardsmay be stored in the OCR system application module 124 or in a locationaccessible by the OCR system application module 124. In certaincircumstances, the data locations may be provided by the issuer 170 ofthe card 102.

In block 215, the OCR system application module 124 extracts relevantdata from the card image. The OCR system application module 124 performsan OCR algorithm on the card image. In the example embodiment, the OCRalgorithm is performed by the OCR system application module 124. Inanother example, the OCR system application module 124 transmits theimage to another suitable computing device for performing the OCRalgorithm.

The OCR system application module 124 may use any suitable algorithm,process, method, or other manner of recognizing data in the card images.The OCR algorithm may represent any suitable process, program, method,or other manner of recognizing the digits or characters represented onthe card image.

In block 220, the OCR system application module 124 extracts relevantdata from the card image. The OCR system application module 124 extractstext, characters, numbers, letters, logos, or other data from the cardimage.

Additionally the OCR system application module 124 extracts data frommachine-readable codes, such as barcodes and QR codes. In the examplesherein, all machine-readable codes will be represented by the term“barcode.” The data in the barcode may be extractable by any machinereading process or algorithm. For example, the height, width, shape, orother characteristics of the barcode and the barcode lines areidentified and decoded by the OCR system application module 124.

In an example embodiment, the OCR system application module 124identifies specific data in the extracted text, such as a redemptioncode or a user account identifier. These and other desired identifiersand codes are referred to herein as “identification codes.”

In block 225, the OCR system application module 124 identifies matchingdata groups in a comparison of an extracted identification code toextracted barcode data. Block 225 is described in greater detail in themethod 225 of FIG. 7.

FIG. 7 is a block flow diagram depicting methods 225 for identifyingmatching data groups in a comparison of an extracted identification codeto extracted barcode data, in accordance with certain exampleembodiments. The term “identification code” represents any desiredseries of characters from the card image. For example, theidentification code may represent a user account number, a redemptioncode, a user identification number, a license number, an expirationdate, or any other suitable code.

In block 705, the OCR system application module 124 identifies theidentification code in the text extracted by the OCR algorithm. Theidentification code may be identified based on the location of theidentification code in the image, based on the section of textcomprising the appropriate number of digits as the identification code,or based on any suitable method of identifying the identification code.The OCR system application module 124 may analyze the extracted text andidentify the identification code from the text.

In block 710, the OCR system application module 124 compares theextracted identification code to extracted barcode data. Data extractedfrom a barcode is typically more accurate than data extracted fromalphanumeric characters. The OCR system application module 124identifies an expected identification code in the extracted text anddetermines the alphanumeric text of the identification code. Forexample, the OCR system application module 124 identifies a series ofextracted characters that follow a pattern of sixteen digits dividedinto groups of four. As this configuration matches a particular accountidentification being sought, then the OCR system application module 124identifies this sixteen digit series as the account identification code.The OCR system application module 124 converts the data in the barcodeto a series of alphanumeric characters to allow the comparison to theextracted text.

In block 715, the OCR system application module 124 identifies matchingdata groups in the comparison. The OCR system application module 124employs a matching algorithm to isolate the identification code withinthe barcode data. For example, the OCR system application module 124searches for a series of characters in the extracted identification codeand the extracted barcode data that match or nearly match.

In the example, if a series of four characters match, then the OCRsystem application module 124 may identify the location of the matchingidentification codes in the extracted text and the extracted barcodedata. For example, if the first four characters of the identificationcodes in the extracted text are 0123, and four digits within the barcodedata are identified as 0123, then the identification code location inthe barcode data may be identified as a 16 digits series beginning withthe identified 0123. The matching of the numbers allows the OCR systemapplication module 124 to determine which of the barcode digits orcharacters correspond to the desired data.

In block 720, the OCR system application module 124 determines if theextracted identification code is an exact match to the barcode data. TheOCR system application module 124 matches the data groups to identifythe identification number or any other suitable number or series ofcharacters that are located in the barcode. With the location of thebarcode number identified, the OCR system application module 124 is ableto correct any errors in the OCR algorithm results.

In an example, a sixteen digit alphanumeric identification code for agift card may be extracted and compared to a thirty digit code extractedfrom the barcode. The OCR system may employ a matching algorithm toidentify a sixteen digit series embedded in the thirty digit code fromthe barcode that matches the identification code. In the example, thesixteen digit identification code is the following series of numbers:

0000 0000 0000 0010

In the example, the barcode data produced the following series ofnumbers:

11 2222 3333 4444 0000 0000 0000 0070

The identification code may be identified based on the matchingalgorithm as the last 16 digits of the barcode. Even though the numbersare not identical, the matching algorithm may determine that the digitsof the extracted text and the last 16 digits of the barcode are likelyto be representative of the same identification code. The determinationmay be based on the fact that fifteen of the sixteen digits match. Iffifteen out of sixteen matching numbers is over a configured thresholdof matching numbers, then the identification code is identified in thebarcode data. In the example, 90% of the numbers must match, and 15 outof 16 is greater than a 90% match. The threshold may be based on acertain percentage of matching numbers or a total number of matchingnumbers. The threshold may be further based on the confidence level thatthe OCR algorithm produced in the result of the unmatched digit or ofthe matched digits. For example, if the OCR algorithm produced a lowconfidence that the unmatched digit was correct, then the OCR systemapplication module 124 may be more likely to consider the digits fromthe barcode to be a match.

In block 735, the OCR system application module 124 corrects theextracted identification code based on any discrepancies with thebarcode data. As the barcode is typically more accurate than extractedtext, the barcode data is considered to be the correct number when adiscrepancy exists between the extracted text and the barcode data. TheOCR system application module 124 changes the stored identification codethat was extracted with the OCR algorithm to exactly match the extractedbarcode data. The corrected identification code is stored.

Thus, in the example, the identification code for the card is correctedto read:

0000 0000 0000 0070

From block 725, the method 225 proceeds to block 230 of FIG. 2.

Returning to FIG. 2, in block 230, the OCR system application module 124supplies the extracted data to a digital wallet account on the OCRsystem application module 124, a digital wallet application module 111,a point of sale terminal, a payment processing system 140, a website, orany suitable application or system that the user 101 desires. Theextracted data may be used by an application on the user computingdevice 110. The extracted data may be transmitted via an Internetconnection over the network 105, via a near field communication (“NFC”)technology, emailed, texted, or transmitted in any suitable manner.

FIG. 3 is a block flow diagram depicting methods 300 for usingclassifier models to provide card metadata, in accordance with certainexample embodiments.

In FIG. 3, blocks 205 through 220 are performed in a substantiallysimilar manner as blocks 205 through 220 of FIG. 2.

In block 325, the OCR system application module 124 classifies a card102 based on a comparison of extracted text to a card model. The OCRsystem application module 124 compares the extracted data to a cardmetadata model to classify the card type. The database or model of cardmetadata model may be a database or other application that stores cardmetadata for one or more card types associated with one or more merchantsystems, card issuers, or other entities. The card metadata may comprisemerchant system names, issuer names, phone numbers, addresses, websites,formats, or other suitable data. In certain embodiments, other dataextracted from the card image may be used, such as images, pictureslogos, or other suitable data. The model may be used to predict otherforms of data relating to the card based on an identification of themodel that is associated with the card.

When the OCR system application module 124 extracts data from a cardimage, the extracted data is compared to the model database. The datafrom the card image is classified based on matches between the extracteddata and the card models. For example, if the extracted data comprises aspecific merchant system name, then the model with that name may beidentified as an appropriate model for the card. For example, the OCRsystem application module 124 may extract, from the model, a merchantsystem address or a merchant system website address.

When a card model classifies the card data, the card model that isassociated with the classification is associated with the card image.Card meta data from the card model is associated with the card image.

In block 330, the OCR system application module 124 supplies cardmetadata from the matching card model in the database to a digitalwallet account. In an example, the metadata from the card model may beused to autofill a form that is being used to add the card to thedigital wallet application module of the user. Autofilling a formincludes any process or method to populate data entry requirements on aform with expected information based on stored information withoutrequiring an input from a user. The form may be autofilled with the datathat is extracted from the card image, the supplied card metadata, or acombination of both the data that is extracted from the card image andthe supplied card metadata. Other data associated with the user 101, theuser account, the merchant system, or other entities may be used toautofill the form.

In another example, the card metadata is transmitted to a digital walletapplication module 111 on the user computing device 110. The data may bedisplayed to the user 101, used to autofill a form, or for any suitablepurpose. The card metadata may be transmitted to a digital walletaccount on the OCR system application module 124 for storage with thecard 102 in the user account. Any other suitable action may be takenwith the card metadata.

In block 330, the user 101 verifies the card metadata. For example, theuser 101 may analyze the autofilled entries in a form displayed on theuser interface via the digital wallet application module 111 or otherapplication on the user computing device 110. The user 101 compares themetadata or the autofilled data with known data or data to which theuser 101 has access from the physical card, from the card image, from athird party location, such as a website, or any other suitable datasource.

In certain embodiments, the user 101 does not agree with the metadatasupplied by the OCR system application module 124. In certain examples,the user 101 corrects the supplied or autofilled data. That is, the datathat was inserted into a data field of the form is either incorrect ornot the preferred entry for the user 101. In the example, the user 101manually alters the data in one or more incorrect fields. The user 101saves the corrected data and registers the card 102 or performs anyother suitable action with the card data. In an example, the user 101changes the phone number for a merchant system associated with a card102 from the supplied number to an alternate number. The user 101 maymake the changes to the data by entering the change via a user interfaceon the user computing device 110 in the digital wallet applicationmodule 111 or any suitable application. The user 101 may make thechanges to the data by entering the change via an Internet connectionwith the user account on the payment processing system 140.

In block 340, the OCR system application module 124 determines ifchanges to the card metadata have been made by the user 101. The OCRsystem application module 124 may determine if changes have been made byreceiving a notification from the digital wallet application module 111of data that has been changed by the user 101. In another example, thepayment processing system 140 may notify the OCR system applicationmodule 124 of the changes. In another example, another application onthe user computing device 110 may monitor the changes and provide thenotification to the OCR system application module 124. Any othersuitable method may be utilized by the OCR system application module 124to determine if changes have been made.

If changes have been made, then the method 300 proceeds to block 345. Ifchanges have not been made, then the method 300 proceeds to block 350.

In block 345, upon a determination that the user 101 changed the cardmetadata, the OCR system application module 124 logs the correctionsmade by the user 101. The OCR system application module 124 may log thechange by the user 101 as the change is made in the user account on theOCR system application module 124. The OCR system application module 124may receive the change when the digital wallet application module 111connects to the OCR system application module 124 to provide acommunication. The OCR system application module 124 may receive thechange from the payment processing system 140 after the user 101 makesthe change on a digital wallet account. The OCR system applicationmodule 124 may received a notification of the change from any suitablesource.

In block 355, the card metadata is saved by the digital wallet account.In an example embodiment, the user 101 actuates a function on the usercomputing device 110 to save the data. For example, the user 101approves the autofilled data in the form for establishing the card 102,and actuates a virtual or physical button that saves the data. The carddata may be saved on the user computing device 110, the paymentprocessing system 140, the OCR system application module 124, a POSterminal of a merchant system, a combination of these locations, or onany suitable computing device.

FIG. 4 is a block flow diagram depicting methods 400 for updating aclassifier model, in accordance with certain example embodiments.

In block 405, the OCR system application module 124 logs corrections forcard metadata from the user computing device 110 and/or other userdevices from one or more other users. As described in block 345 of FIG.4, the OCR system application module 124 receives notifications when auser 101 corrects metadata supplied by the OCR system application module124.

In block 410, the OCR system application module 124 analyzes the loggeddata for a particular card 102. The OCR system application module 124may periodically or continually note corrections, log the number ofcorrections, log the content being corrected, log the revised data thatis used, and note when the corrections correlate to corrections fromother users.

In block 415, the OCR system application module 124 determines ifreceived corrections are greater than a preconfigured threshold numberof corrections. In an example, the OCR system application module 124determines that 20 users have changed the phone number for a merchantsystem associated with a card 102 from the provided number to analternate number. The OCR system application module 124 determines thateither the provided number was incorrect or that the merchant system haschanged the phone number. The OCR system application module 124 updatesthe metadata model associated with the merchant system to reflect thechanged model.

The threshold number of user corrections required before the OCR systemapplication module 124 takes action may be configured by an operator onthe OCR system application module 124, by an algorithm devised tominimize the incorrect data, or by any suitable system, person, program,or other party. In certain embodiments, the OCR system applicationmodule 124 may take action after as few as one correction. In certainembodiments, the action taken by the OCR system application module 124may only be to monitor future interactions associated with the correctedcard 102. In certain embodiments, the action taken by the OCR systemapplication module 124 may be to suspend the card model until correctdata is determined by a third party, such as OCR system applicationmodule 124 operator.

In block 420, the OCR system application module 124 retrains the cardmetadata database. When the OCR system application module 124 determinesthat an amount of corrections over the threshold have occurred, the OCRsystem application module 124 retrains the card metadata database suchthat future data sent with a particular card model will comprise theupdated data. In the previous example, the OCR system application module124 updates the phone number of the merchant system to reflect the newphone number.

In block 425, the OCR system application module 124 evaluates the cardmetadata database. The OCR system application module 124 may evaluatethe updated card models on the database by providing the updatedmetadata to subsequent users when subsequent users are presented withmetadata based on OCR of an image of a similar card 102 to determine ifthe subsequent users correct the data. In another example, an operatorof the OCR system application module 124 compares the updated data to athird party source. For example, the operator of the OCR systemapplication module 124 may compare the updated data to the datadisplayed on a website of the merchant system.

In block 430, the OCR system application module 124 updates the cardmetadata database for future users 102. The updated card metadatadatabase may provide the updated phone number to subsequent users 102.

FIG. 5 is a block flow diagram depicting methods 500 for extracting carddata using a determined card type, in accordance with certain exampleembodiments.

Blocks 205 through 220 are substantially similar to blocks 205 through220 of FIG. 2.

Block 525 is described in greater detail in the method 525 of FIG. 8.

FIG. 8 is a block flow diagram depicting methods for determining whethertext or barcode data is a more accurate card identification location, inaccordance with certain example embodiments.

In block 805, the OCR system application module 124 analyzes theextracted data from the card image. The OCR system application module124 analyzes the extracted text from a card image and identifies detailsrelated to the card 102 based on identified factors. The factors may bea recognized merchant system name, a recognized issuer name, arecognized format, a recognized identification number sequence, arecognized logo, a recognized color scheme, a recognized phone number,or any other suitable factor. In an example, the OCR system applicationmodule 124 may identify text on the card image that matches a name of amerchant system in the database. The analysis is used by the OCR systemapplication module 124 to identify, from a comparison with a database,the name of a particular bank, merchant system, card issuer, or anysuitable institution.

In an example, the OCR system application module 124 may identify thecard image as likely being a debit card associated with the particularmerchant system based on an identification of the merchant system andthe word “debit” on the card.

In block 810, the OCR system application module 124 determines a cardtype based on detected data in the card image. When the OCR systemapplication module 124 identifies one or more factors from the cardimage as matching factors stored in a database on the OCR system 120,the OCR system application module 124 identifies the card type based ondata associated with the matched factors. The card type may be a generaltype for a debit card, a gift card, or other card type. The card typemay be a format for a specific merchant system or issuer. The card typemay be for a standard card format for an identification card or an entryticket. The card type may be any known card format or other standard fora group of issued cards.

In the previous example, a debit card associated with a particularmerchant system is identified. The OCR system application module 124accesses information about the card type associated with the debit cardassociated with the particular merchant system from a database of cardtypes. The database may be stored on the OCR system 124, stored by aremote system, stored in a cloud computing environment, or stored in anysuitable location.

In block 815, the OCR system application module 124 identifies aconfiguration of data in the card type. Knowledge of the card typeallows the OCR system application module 124 to access information aboutthe card 102, such as the format of the identification number, thelocation of data on the card, the format of a barcode, and othersuitable information. As the card type may be associated with a formatfor the data displayed on the card, the OCR system application module124 is able to accurately identify required data in a specifiedlocation. The configuration of the data in the card type may be storedon the database of card types. The OCR system application module 124accesses the configuration from the database based on the knowledge ofthe card type.

In block 820, the OCR system application module 124 uses the card typeformat to determine the most accurate card identification location. Theknown format of a card 102 allows the data extraction to be performedmore accurately based on known extraction techniques and the accuracy ofthe techniques. For example, OCR algorithms of an image may have anaccuracy of 80% based on industry standards while barcode extraction mayhave an accuracy of 98% based on industry standards. The OCR systemapplication module 124 compares the locations of required informationand determines the location that is most accurate. Some locations may bemore accurate, easier to obtain, or in any manner more advantageous thananother location. For example, if a number is represented in a barcodeand in human-readable text on the card, the barcode may provide a moreaccurate data extraction.

In an example, if the OCR system application module 124 determines thatthe card 102 has a format wherein a redemption code for a gift card is a16 digit code at the end of the string of characters represented on thebarcode, then the OCR system application module 124 will be able toeasily and accurately extract the redemption code. In certain examples,the OCR system application module 124 determines that the card 102 has aformat wherein a redemption code for a gift card is represented in thebarcode and also in human-readable text on the card. The OCR systemapplication module 124 may identify information that specifies whichlocation provides the most accurate results.

In another example, the OCR system application module 124 may identify achecksum rule for the account number on a card. If the OCR systemapplication module 124 extracts an account number, the OCR systemapplication module 124 may then apply the appropriate checksum algorithmto verify the account number of the card 102.

In alternate embodiments, the OCR system application module 124 may becustomized to look for characters in particular locations on the cardimage based on the card type. The OCR system application module 124 maybe customized to look for a certain number of characters. The OCR systemapplication module 124 may be customized to look for certaincombinations of characters. The OCR system application module 124 may becustomized to know that the cards 102 from the particular credit cardcompany typically have certain data on the reverse side of the card. TheOCR system may be customized to know which characters are typicallyembossed. The OCR system may be customized to look for any configuredarrangements, data locations, limitations, card types, characterconfigurations, or other suitable card data.

From block 820, the method 525 proceeds to block 240 in FIG. 5. Block240 is substantially similar to block 240 as described previously withrespect to FIG. 2.

Other Example Embodiments

FIG. 9 depicts a computing machine 2000 and a module 2050 in accordancewith certain example embodiments. The computing machine 2000 maycorrespond to any of the various computers, servers, mobile devices,embedded systems, or computing systems presented herein. The module 2050may comprise one or more hardware or software elements configured tofacilitate the computing machine 2000 in performing the various methodsand processing functions presented herein. The computing machine 2000may include various internal or attached components such as a processor2010, system bus 2020, system memory 2030, storage media 2040,input/output interface 2060, and a network interface 2070 forcommunicating with a network 2080.

The computing machine 2000 may be implemented as a conventional computersystem, an embedded controller, a laptop, a server, a mobile device, asmartphone, a set-top box, a kiosk, a vehicular information system, onemore processors associated with a television, a customized machine, anyother hardware platform, or any combination or multiplicity thereof. Thecomputing machine 2000 may be a distributed system configured tofunction using multiple computing machines interconnected via a datanetwork or bus system.

The processor 2010 may be configured to execute code or instructions toperform the operations and functionality described herein, managerequest flow and address mappings, and to perform calculations andgenerate commands. The processor 2010 may be configured to monitor andcontrol the operation of the components in the computing machine 2000.The processor 2010 may be a general purpose processor, a processor core,a multiprocessor, a reconfigurable processor, a microcontroller, adigital signal processor (“DSP”), an application specific integratedcircuit (“ASIC”), a graphics processing unit (“GPU”), a fieldprogrammable gate array (“FPGA”), a programmable logic device (“PLD”), acontroller, a state machine, gated logic, discrete hardware components,any other processing unit, or any combination or multiplicity thereof.The processor 2010 may be a single processing unit, multiple processingunits, a single processing core, multiple processing cores, specialpurpose processing cores, co-processors, or any combination thereof.According to certain example embodiments, the processor 2010 along withother components of the computing machine 2000 may be a virtualizedcomputing machine executing within one or more other computing machines.

The system memory 2030 may include non-volatile memories such asread-only memory (“ROM”), programmable read-only memory (“PROM”),erasable programmable read-only memory (“EPROM”), flash memory, or anyother device capable of storing program instructions or data with orwithout applied power. The system memory 2030 may also include volatilememories such as random access memory (“RAM”), static random accessmemory (“SRAM”), dynamic random access memory (“DRAM”), and synchronousdynamic random access memory (“SDRAM”). Other types of RAM also may beused to implement the system memory 2030. The system memory 2030 may beimplemented using a single memory module or multiple memory modules.While the system memory 2030 is depicted as being part of the computingmachine 2000, one skilled in the art will recognize that the systemmemory 2030 may be separate from the computing machine 2000 withoutdeparting from the scope of the subject technology. It should also beappreciated that the system memory 2030 may include, or operate inconjunction with, a non-volatile storage device such as the storagemedia 2040.

The storage media 2040 may include a hard disk, a floppy disk, a compactdisc read only memory (“CD-ROM”), a digital versatile disc (“DVD”), aBlu-ray disc, a magnetic tape, a flash memory, other non-volatile memorydevice, a solid state drive (“SSD”), any magnetic storage device, anyoptical storage device, any electrical storage device, any semiconductorstorage device, any physical-based storage device, any other datastorage device, or any combination or multiplicity thereof. The storagemedia 2040 may store one or more operating systems, application programsand program modules such as module 2050, data, or any other information.The storage media 2040 may be part of, or connected to, the computingmachine 2000. The storage media 2040 may also be part of one or moreother computing machines that are in communication with the computingmachine 2000 such as servers, database servers, cloud storage, networkattached storage, and so forth.

The module 2050 may comprise one or more hardware or software elementsconfigured to facilitate the computing machine 2000 with performing thevarious methods and processing functions presented herein. The module2050 may include one or more sequences of instructions stored assoftware or firmware in association with the system memory 2030, thestorage media 2040, or both. The storage media 2040 may thereforerepresent examples of machine or computer readable media on whichinstructions or code may be stored for execution by the processor 2010.Machine or computer readable media may generally refer to any medium ormedia used to provide instructions to the processor 2010. Such machineor computer readable media associated with the module 2050 may comprisea computer software product. It should be appreciated that a computersoftware product comprising the module 2050 may also be associated withone or more processes or methods for delivering the module 2050 to thecomputing machine 2000 via the network 2080, any signal-bearing medium,or any other communication or delivery technology. The module 2050 mayalso comprise hardware circuits or information for configuring hardwarecircuits such as microcode or configuration information for an FPGA orother PLD.

The input/output (“I/O”) interface 2060 may be configured to couple toone or more external devices, to receive data from the one or moreexternal devices, and to send data to the one or more external devices.Such external devices along with the various internal devices may alsobe known as peripheral devices. The I/O interface 2060 may include bothelectrical and physical connections for operably coupling the variousperipheral devices to the computing machine 2000 or the processor 2010.The I/O interface 2060 may be configured to communicate data, addresses,and control signals between the peripheral devices, the computingmachine 2000, or the processor 2010. The I/O interface 2060 may beconfigured to implement any standard interface, such as small computersystem interface (“SCSI”), serial-attached SCSI (“SAS”), fiber channel,peripheral component interconnect (“PCI”), PCI express (PCIe), serialbus, parallel bus, advanced technology attached (“ATA”), serial ATA(“SATA”), universal serial bus (“USB”), Thunderbolt, FireWire, variousvideo buses, and the like. The I/O interface 2060 may be configured toimplement only one interface or bus technology. Alternatively, the I/Ointerface 2060 may be configured to implement multiple interfaces or bustechnologies. The I/O interface 2060 may be configured as part of, allof, or to operate in conjunction with, the system bus 2020. The I/Ointerface 2060 may include one or more buffers for bufferingtransmissions between one or more external devices, internal devices,the computing machine 2000, or the processor 2010.

The I/O interface 2060 may couple the computing machine 2000 to variousinput devices including mice, touch-screens, scanners, electronicdigitizers, sensors, receivers, touchpads, trackballs, cameras,microphones, keyboards, any other pointing devices, or any combinationsthereof. The I/O interface 2060 may couple the computing machine 2000 tovarious output devices including video displays, speakers, printers,projectors, tactile feedback devices, automation control, roboticcomponents, actuators, motors, fans, solenoids, valves, pumps,transmitters, signal emitters, lights, and so forth.

The computing machine 2000 may operate in a networked environment usinglogical connections through the network interface 2070 to one or moreother systems or computing machines across the network 2080. The network2080 may include wide area networks (WAN), local area networks (LAN),intranets, the Internet, wireless access networks, wired networks,mobile networks, telephone networks, optical networks, or combinationsthereof. The network 2080 may be packet switched, circuit switched, ofany topology, and may use any communication protocol. Communicationlinks within the network 2080 may involve various digital or an analogcommunication media such as fiber optic cables, free-space optics,waveguides, electrical conductors, wireless links, antennas,radio-frequency communications, and so forth.

The processor 2010 may be connected to the other elements of thecomputing machine 2000 or the various peripherals discussed hereinthrough the system bus 2020. It should be appreciated that the systembus 2020 may be within the processor 2010, outside the processor 2010,or both. According to some embodiments, any of the processor 2010, theother elements of the computing machine 2000, or the various peripheralsdiscussed herein may be integrated into a single device such as a systemon chip (“SOC”), system on package (“SOP”), or ASIC device.

In situations in which the systems discussed here collect personalinformation about users, or may make use of personal information, theusers may be provided with an opportunity or option to control whetherprograms or features collect user information (e.g., information about auser's social network, social actions or activities, profession, auser's preferences, or a user's current location), or to control whetherand/or how to receive content from the content server that may be morerelevant to the user. In addition, certain data may be treated in one ormore ways before it is stored or used, so that personally identifiableinformation is removed. For example, a user's identity may be treated sothat no personally identifiable information can be determined for theuser, or a user's geographic location may be generalized where locationinformation is obtained (such as to a city, ZIP code, or state level),so that a particular location of a user cannot be determined. Thus, theuser may have control over how information is collected about the userand used by a content server.

Embodiments may comprise a computer program that embodies the functionsdescribed and illustrated herein, wherein the computer program isimplemented in a computer system that comprises instructions stored in amachine-readable medium and a processor that executes the instructions.However, it should be apparent that there could be many different waysof implementing embodiments in computer programming, and the embodimentsshould not be construed as limited to any one set of computer programinstructions. Further, a skilled programmer would be able to write sucha computer program to implement an embodiment of the disclosedembodiments based on the appended flow charts and associated descriptionin the application text. Therefore, disclosure of a particular set ofprogram code instructions is not considered necessary for an adequateunderstanding of how to make and use embodiments. Further, those skilledin the art will appreciate that one or more aspects of embodimentsdescribed herein may be performed by hardware, software, or acombination thereof, as may be embodied in one or more computingsystems. Moreover, any reference to an act being performed by a computershould not be construed as being performed by a single computer as morethan one computer may perform the act.

The example embodiments described herein can be used with computerhardware and software that perform the methods and processing functionsdescribed herein. The systems, methods, and procedures described hereincan be embodied in a programmable computer, computer-executablesoftware, or digital circuitry. The software can be stored oncomputer-readable media. For example, computer-readable media caninclude a floppy disk, RAM, ROM, hard disk, removable media, flashmemory, memory stick, optical media, magneto-optical media, CD-ROM, etc.Digital circuitry can include integrated circuits, gate arrays, buildingblock logic, field programmable gate arrays (FPGA), etc.

The example systems, methods, and acts described in the embodimentspresented previously are illustrative, and, in alternative embodiments,certain acts can be performed in a different order, in parallel with oneanother, omitted entirely, and/or combined between different exampleembodiments, and/or certain additional acts can be performed, withoutdeparting from the scope and spirit of various embodiments. Accordingly,such alternative embodiments are included in the invention claimedherein.

Although specific embodiments have been described above in detail, thedescription is merely for purposes of illustration. It should beappreciated, therefore, that many aspects described above are notintended as required or essential elements unless explicitly statedotherwise. Modifications of, and equivalent components or actscorresponding to, the disclosed aspects of the example embodiments, inaddition to those described above, can be made by a person of ordinaryskill in the art, having the benefit of the present disclosure, withoutdeparting from the spirit and scope of embodiments defined in thefollowing claims, the scope of which is to be accorded the broadestinterpretation so as to encompass such modifications and equivalentstructures.

What is claimed is:
 1. A computer-implemented method to extract cardinformation, comprising: performing, by the one or more computingdevices, an optical character recognition algorithm on a digitalrepresentation of a card; performing, by the one or more computingdevices, a data recognition algorithm on a machine-readable code on thedigital representation of the card; comparing, by the one or morecomputing devices, a series of extracted alphanumeric charactersobtained via the optical character recognition process to data extractedfrom the machine-readable code via the data recognition process;determining, by the one or more computing devices, a match between thealphanumeric series of characters determined via the optical characterrecognition process performed on the digital representation of the cardto a particular series of characters extracted from the data extractedfrom the machine-readable code; determining, by the one or morecomputing devices, if the alphanumeric series determined via the opticalcharacter recognition process performed on the digital representation ofthe card and the matching series of characters extracted from the dataextracted from the machine-readable code comprise any discrepancies;upon a determination that a discrepancy between the alphanumeric seriesand the machine-readable code exists, correcting, by the one or morecomputing devices, the alphanumeric series of characters determined viathe optical character recognition process performed on the digitalrepresentation of the card based on the particular series of charactersextracted from the machine-readable code; and communicating, by the oneor more computing devices and to the user computing device, thecorrected alphanumeric series and instructions to display the correctedalphanumeric series via the user computing device.
 2. The method ofclaim 1, wherein the series of extracted alphanumeric characters and theparticular series of characters extracted from the data extracted fromthe machine-readable code are determined to match based on anidentification, by the one or more computing devices, of at least athreshold number of matching characters between the extractedalphanumeric characters and the particular series of charactersextracted from the data extracted from the machine-readable code.
 3. Themethod of claim 2, wherein the threshold is based on a percentage of thecharacters matching.
 4. The method of claim 1, wherein themachine-readable code comprises a barcode.
 5. The method of claim 1,wherein the alphanumeric series of characters comprises anidentification code for an account of a user.
 6. The method of claim 1,wherein the alphanumeric series of characters comprises a redemptioncode of a gift card.
 7. A computer program product, comprising: anon-transitory computer-readable storage device having computer-readableprogram instructions embodied thereon that when executed by a computercause the computer to extract card information, comprising:computer-readable program instructions to perform an optical characterrecognition algorithm on a digital representation of a card;computer-readable program instructions to perform a data recognitionalgorithm on a machine-readable code on the digital representation ofthe card; computer-readable program instructions to compare a series ofextracted alphanumeric characters obtained via the optical characterrecognition process to data extracted from the machine-readable code viathe data recognition process; computer-readable program instructions todetermine a match between the alphanumeric series of charactersdetermined via the optical character recognition process performed onthe digital representation of the card to a particular series ofcharacters extracted from the data extracted from the machine-readablecode; computer-readable program instructions to determine if thealphanumeric series determined via the optical character recognitionprocess performed on the digital representation of the card and thematching series of characters extracted from the data extracted from themachine-readable code comprise any discrepancies; and in response todetermining that a discrepancy between the alphanumeric series and themachine-readable code exists, computer-readable program instructions tocorrect the alphanumeric series of characters determined via the opticalcharacter recognition process performed on the digital representation ofthe card based on the particular series of characters extracted from thedata extracted from the machine-readable code upon determination that adiscrepancy exists.
 8. The computer program product of claim 7, whereinthe series of extracted alphanumeric characters and the particularseries of characters extracted from the data extracted from themachine-readable code are determined to match based on computer-readableprogram instructions to identify at least a threshold number of matchingcharacters between the extracted alphanumeric characters and theparticular series of characters extracted from the data extracted fromthe machine-readable code.
 9. The computer program product of claim 8,wherein the threshold is based on a percentage of the charactersmatching.
 10. The computer program product of claim 7, furthercomprising computer-readable program instructions to communicate, to theuser computing device, the corrected alphanumeric series for display.11. The computer program product of claim 7, wherein themachine-readable code comprises a barcode.
 12. The computer programproduct of claim 7, wherein the alphanumeric series of characterscomprises an identification code for an account of a user.
 13. Thecomputer program product of claim 7, wherein the alphanumeric series ofcharacters comprises a redemption code of a gift card.
 14. A system toextract card information, comprising: a storage device; and a processorcommunicatively coupled to the storage device, wherein the processorexecutes application code instructions that are stored in the storagedevice to cause the system to: perform an optical character recognitionalgorithm on a digital representation of a card; perform a datarecognition algorithm on a machine-readable code on the digitalrepresentation of the card; compare a series of extracted alphanumericcharacters obtained via the optical character recognition process todata extracted from the machine-readable code via the data recognitionprocess; determine a match between the alphanumeric series of charactersdetermined via the optical character recognition process performed onthe digital representation of the card to a particular series ofcharacters extracted from the data extracted from the machine-readablecode; determine if the alphanumeric series determined via the opticalcharacter recognition process performed on the digital representation ofthe card and the matching series of characters extracted from the dataextracted from the machine-readable code comprise any discrepancies; andin response to determining that a discrepancy between the alphanumericseries and the machine-readable code exists, correct the alphanumericseries of characters determined via the optical character recognitionprocess performed on the digital representation of the card based on theparticular series of characters extracted from the data extracted fromthe machine-readable code upon a determination that a discrepancyexists.
 15. The system of claim 14, wherein the series of extractedalphanumeric characters and the particular series of charactersextracted from the data extracted from the machine-readable code aredetermined to match based on identifying at least a threshold number ofmatching characters between the extracted alphanumeric characters andthe particular series of characters extracted from the data extractedfrom the machine-readable code.
 16. The system of claim 15, wherein thealphanumeric series of characters comprises a redemption code of a giftcard.
 17. The system of claim 14, further comprising application codeinstructions to cause the system to communicate, to the user computingdevice, the corrected alphanumeric series for display.
 18. The system ofclaim 14, wherein the machine-readable code comprises a barcode.
 19. Thesystem of claim 14, wherein the alphanumeric series of characterscomprises an identification code for an account of a user.